Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1785

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Author: search.filters.author.Özlük, ÖzgürSubject: search.filters.subject.Makine Öğrenmesi

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  • Master Term Project
    Tractor Sales Forecast Using Machine Learning
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tunay, Yiğitcan; Özlük, Özgür
    This study presents a machine learning model to forecast tractor sales using four years of number of tractor sales based on year, month, city, town, brand and model provided by Turkey Statistical Institute. Tractor sales can vary depending on many different factors. Therefore, it is a challenging task for any company to estimate number of tractor sales that will be sold next year. Having the ability to predict that accurately will contribute companies in many distinct ways. Foreseeing market trends, keeping pace with the competition, delivering the right product to the right customer at the right time, reducing inventory costs, better production planning and cash flow management are major advantages of accurate forecasting. Within the scope of this study, models were developed to predict tractor sales using different statistical and machine learning methods. In further steps of the study, meaningful variables can be added to the dataset in order to reach a better result. Also, market share can be estimated by using different simulation methods which take into consideration those variables.
  • Master Term Project
    Second-Hand Car Price Estimation Using Machine Learning
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Kütükde, Şule; Özlük, Özgür
    The ones who think to sell their cars always think about their cars’ second-hand market worth, at first. Both for the sellers and the buyers, it is crucially important to estimate the car’s realistic worth, in order not to suffer a loss of money or time. In this research, arabam.com’s advertisement data is obtained with the help of web scraping technique, and later machine learning algorithms like Linear Regression, Decision Tree, Random Forest and Gradient Boosting are applied for collected advertisement data in order to estimate cars’ prices. In addition, some hyperparameter tuning is applied for robust estimation. The models’ performances are discussed, and some remarks offered for further researches.